collection of diffusion model papers categorized by their subareas
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Updated
Jun 12, 2024
collection of diffusion model papers categorized by their subareas
[EMNLP 2022] An Open Toolkit for Knowledge Graph Extraction and Construction
A PyTorch Library for Meta-learning Research
Implementation of the model-agnostic meta-learning framework on CWRU bearing fault dataset to address cross-domain few-shot fault diagnosis problem.
Official PyTorch implementation of SynergyNeRF: "Synergistic Integration of Coordinate Network and Tensorial Feature for Improving NeRFs from Sparse Inputs (ICML2024)"
Official implementation of the paper 'Exploring Robust Features for Few-Shot Object Detection in Satellite Imagery'
Zero and Few shot named entity & relationships recognition
[AAAI-2024] Pytorch implementation of "ColNeRF: Collaboration for Generalizable Sparse Input Neural Radiance Field"
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
A new comprehensive and diverse few-shot acoustic classification benchmark.
An official code for paper: TFPred: Learning discriminative representations from unlabeled data for few-label rotating machinery fault diagnosis
FSL-Mate: A collection of resources for few-shot learning (FSL).
A transfer learning fault diagnosis repository covering popular algorithms
A unified multi-task time series model.
Implementation of NAACL 2024 main conference paper: Named Entity Recognition Under Domain Shift via Metric Learning for Life Science
Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning. NAACL 2024.
Official implementation of the EACL Findings 2024 paper: Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction
Code Repository for "SSL-ProtoNet: Self-supervised Learning Prototypical Networks for few-shot learning"
A quick reimplementation of the two datasets ("digits" and "commands") proposed in the paper "An Investigation of Few-Shot Learning in Spoken Term Classification"
[EMNLP 2022 Findings] Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study
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